Fire Lab tree list: A tree-level model of the conterminous United States landscape circa 2014

Observations of the forests of the conterminous United States at the level of individual trees would be of utility for any number of applications, ranging from modelling the effect of wildland fire on terrestrial carbon resources to estimation of timber volume. While such observations do exist at selected spots such as established forest plots, most forests have not been mapped with this level of specificity. To fill the gap in tree-level mapping, we used a modelling approach that employed a random forests machine-learning technique. This technique was nearly identical to that employed by Riley et al. (2016), except that it used disturbance variables in addition to topographic and biophysical variables. This method imputes the plot with the best statistical match, according to a “forest” of decision trees, to each pixel of gridded landscape data. A set of predictor variables was used to train the random forests algorithm, which was then leveraged to extrapolate measurements across forested areas of the conterminous United States. Specifically, predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2014. These variables were present or were derived for both 1) the detailed reference data, which consisted of forest plot data from the U.S. Forest Service’s Forest and Inventory Analysis program (FIA) version 1.7.1 and 2) the landscape target data, which consisted of raster data at 30x30 meter (m) resolution provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE; https://landfire.gov/) FIA plots were imputed to the raster data by the random forests algorithm, providing a tree-level model of all forested areas in the conterminous U.S. Of 67,141 single-condition FIA plots available to random forests, 62,758 of these (93.5%) were utilized in imputation to 2,841,601,981 forested pixels. The main output of this project (the GeoTIFF available in this data publication) is a map of imputed plot identifiers at 30×30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2014. This map is commonly known as "TreeMap 2014". The map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://apps.fs.usda.gov/fia/datamart/datamart_access.html) or to the Microsoft Access Database and ASCII files included in this data publication to produce tree-level maps or to map other plot attributes. These files also contain attributes regarding the FIA PLOT CN (a unique identifier for each time a plot is measured), the inventory year, the state code and abbreviation, the unit code, the county code, the plot number, the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a unique identifier for each tree and tree visit. Application of the dataset to research questions other than those related to aboveground biomass and carbon should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.
Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. Such detailed forest structure data are not available for large areas of public and private lands in the United States, which rely on forest inventory at fixed plot locations at sparse densities. While direct sampling technologies such as light detection and ranging (LiDAR) may eventually make broad coverage of detailed forest inventory feasible, no such data sets at the scale of the conterminous United States (CONUS) are currently available.
See the Entity and Attributes section for details regarding the relationship between the data files included in this publication and the FIA DataMart. These data were published on 07/02/2019. On 03/26/2021, the metadata was updated to include reference to a new publication. On 02/01/2024, some additional minor metadata updates were made and trees_CONUS_5_15_2019.mdb was removed from the package because it is an older format and the same content is included via text files.